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Monocular Depth Estimation

Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity. The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error.

Source: Defocus Deblurring Using Dual-Pixel Data

Papers

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Showing 501–550 of 876 papers

TitleStatusHype
Calibrating Self-supervised Monocular Depth Estimation—0
Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation—0
Camera-Only Bird's Eye View Perception: A Neural Approach to LiDAR-Free Environmental Mapping for Autonomous Vehicles—0
CamLessMonoDepth: Monocular Depth Estimation with Unknown Camera Parameters—0
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation—0
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss—0
CLIP Can Understand Depth—0
CoL3D: Collaborative Learning of Single-view Depth and Camera Intrinsics for Metric 3D Shape Recovery—0
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation—0
Composite Learning for Robust and Effective Dense Predictions—0
Connecting the Dots: Learning Representations for Active Monocular Depth Estimation—0
Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR—0
CroMo: Cross-Modal Learning for Monocular Depth Estimation—0
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection—0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy—0
Deep 3D Pan via adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep 3D Pan via Local adaptive "t-shaped" convolutions with global and local adaptive dilations—0
Deep Classification Network for Monocular Depth Estimation—0
Deep Depth From Aberration Map—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications—0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images—0
Deep multi-scale architectures for monocular depth estimation—0
Deep Neural Networks for Accurate Depth Estimation with Latent Space Features—0
Deep Optics for Monocular Depth Estimation and 3D Object Detection—0
Deep Planar Parallax for Monocular Depth Estimation—0
Deflating Dataset Bias Using Synthetic Data Augmentation—0
Dense Geometry Supervision for Underwater Depth Estimation—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
Dense monocular Simultaneous Localization and Mapping by direct surfel optimization—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Depth Anything in Medical Images: A Comparative Study—0
Depth Anything with Any Prior—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
DepthART: Monocular Depth Estimation as Autoregressive Refinement Task—0
Exploring Depth Contribution for Camouflaged Object Detection—0
Depth Estimation and Image Restoration by Deep Learning from Defocused Images—0
Depth Estimation Based on 3D Gaussian Splatting Siamese Defocus—0
Depth Estimation from Single Image using Sparse Representations—0
Depth Estimation with Simplified Transformer—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions—0
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation—0
Depth Is All You Need for Monocular 3D Detection—0
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